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2007
Springer

Improving Frequent Subgraph Mining in the Presence of Symmetry

9 years 3 months ago
Improving Frequent Subgraph Mining in the Presence of Symmetry
While recent algorithms for mining the frequent subgraphs of a database are efficient in the general case, these algorithms tend to do poorly on databases that have a few or no labels. Although little attention has been given to such datasets, there are many existing applications which deal with this type of data. In this paper, we present a novel algorithm, called SyGMA, that improves frequent subgraph mining in such cases by limiting the impact of symmetry on calculations, without the use of memory-expensive structures. Through experimentation on various datasets, we show that our algorithm outperforms, in many cases, one of the leading algorithms for this task.
Christian Desrosiers, Philippe Galinier, Pierre Ha
Added 08 Jun 2010
Updated 08 Jun 2010
Type Conference
Year 2007
Where MLG
Authors Christian Desrosiers, Philippe Galinier, Pierre Hansen, Alain Hertz
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